307 research outputs found

    Tabu Search: A Comparative Study

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    Optimal route planning of agricultural field operations using ant colony optimization

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    Farming operations efficiency is a crucial factor that determines the overall operational cost in agricultural production systems.  Improved efficiency can be achieved by implementing advanced planning methods for the execution of field operations dealing, especially with the routing and area coverage optimisation aspects. Recently, a new type of field area coverage patterns, the B-patterns, has been introduced.  B-patterns are the result of a combinatorial optimisation process that minimizes operational criterions such as, the operational time, non-working travelled distance, fuel consumption etc.  In this paper an algorithmic approach for the generation of B-patterns based on ant colony optimisation is presented.  Ant colony optimization metaheuristic was chosen for the solution of the graph optimisation problem inherent in the generation of B-patterns.  Experimental results on two selected fields were presented for the demonstration of the effectiveness of the proposed approach. Based on the results, it was shown that it is feasible to use ant colony optimization for the generation of optimal routes for field area coverage while tests made on the resulting routes indicated that they can be followed by any farm machine equipped with auto-steering and navigation systems

    Waste Collection Vehicle Routing Problem: Literature Review

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    Waste generation is an issue which has caused wide public concern in modern societies, not only for the quantitative rise of the amount of waste generated, but also for the increasing complexity of some products and components. Waste collection is a highly relevant activity in the reverse logistics system and how to collect waste in an efficient way is an area that needs to be improved. This paper analyzes the major contribution about Waste Collection Vehicle Routing Problem (WCVRP) in literature. Based on a classification of waste collection (residential, commercial and industrial), firstly the key findings for these three types of waste collection are presented. Therefore, according to the model (Node Routing Problems and Arc Routing problems) used to represent WCVRP, different methods and techniques are analyzed in this paper to solve WCVRP. This paper attempts to serve as a roadmap of research literature produced in the field of WCVRP

    GAPS : a hybridised framework applied to vehicle routing problems

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    In this thesis we consider two combinatorial optimisation problems; the Capacitated Vehicle Routing Problem (CVRP) and the Capacitated Arc Routing Problem (CARP). In the CVRP, the objective is to find a set of routes for a homogenous fleet of vehicles, which must service a set of customers from a central depot. In contrast, the CARP requires a set of routes for a fleet of vehicles to service a set of customers at the street level of an intercity network. After a comprehensive discussion of the existing exact and heuristic algorithmic techniques presented in the literature for these problems, computational experiments to provide a benchmark comparison of a subset of algorithmic implementations for these methods are presented for both the CVRP and CARP, run against a series of dataset instances from the literature. All dataset instances are re-catalogued using a standard format to overcome the difficulties of the different naming schemes and duplication of instances that exist between different sources. We then present a framework, which we shall call Genetic Algorithm with Perturbation Scheme (GAPS), to solve a number of combinatorial optimisation problems. The idea is to use a genetic algorithm as a container framework in conjunction with a perturbation or weight coding scheme. These schemes make alterations to the underlying input data within a problem instance, after which the changed data is fed into a standard problem specific heuristic and the solution obtained decoded to give a true solution cost using the original unaltered instance data. We first present GAPS in a generic context, using the Travelling Salesman Problem (TSP) as an example and then provide details of the specific application of GAPS to both the CVRP and CARP. Computational experiments on a large set of problem instances from the literature are presented and comparisons with the results achieved by the current state of the art algorithmic approaches for both problems are given, highlighting the robustness and effectiveness of the GAPS framework.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Problèmes de tournées en viabilité hivernale utilisant la prévision des volumes d’épandage

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    RÉSUMÉ : Cette thèse combine deux domaines de recherche différents appliqués au déneigement : la recherche opérationnelle et la science des données. La science des données a été utilisée pour développer un modèle de prédiction de quantité de sel et d’abrasif avec une méthodologie d’apprentissage machine; par la suite, ce modèle est pris en compte pour la confection des tournées de véhicules. La confection des tournées a été élaborée en utilisant des outils de la recherche opérationnelle, qui servent à optimiser les tournées en considérant plusieurs contraintes et en intégrant les données réelles. La thèse est le fruit d’une collaboration avec deux villes québécoises, Granby et Saint-Jean-sur- Richelieu. Elle traite une application réelle en viabilité hivernale, qui est l’opération d’épandage. Cette opération est une activité nécessaire, dont le but est d’assurer une meilleure circulation routière. Cependant, cela se réalise avec un coût économique et environnemental important. Par conséquent, la réduction de ce coût devient une grande préoccupation. Cette thèse contribue significativement aux opérations d’épandage : premièrement, nous prédisons la quantité nécessaire de sel et d’abrasif à épandre afin d’éviter le surépandage; deuxièmement, nous optimisons les tournées des opérations d’épandage en considérant la variation de la quantité. La première contribution de cette thèse consiste en un modèle de prédiction des quantités de sel et d’abrasif pour chaque segment de rue et pour chaque heure, en utilisant des algorithmes d’apprentissage machine. L’importance de cette contribution réside d’une part dans l’intégration des données géomatiques avec les données météo-routières, et d’autre part dans l’extraction des variables importantes (feature engineering) pour le modèle de prédiction. Plusieurs algorithmes d’apprentissage machine ont été évalués : (les forêts aléatoires, les arbres extrêmement aléatoires, les réseaux de neurones artificiels, Adaboost, Gradient Boosting Machine et XGBoost). Le modèle élaboré par XGBoost a réalisé une meilleure performance. Le modèle de prédiction permet non seulement de prédire les quantités de sel et d’abrasif nécessaires à épandre mais aussi, d’identifier les variables les plus importantes pour la prédiction. Cette information représente un outil de décision intéressant pour les gestionnaires. L’identification des variables importantes pourrait améliorer les opérations de déneigement. D’après les résultats trouvés, le facteur humain (conducteur) influence significativement la quantité d’épandage; donc, le contrôle de ce facteur peut améliorer considérablement ces opérations. La deuxième contribution introduit un nouveau problème dans la littérature : le problème de tournées de véhicules générales avec capacité dont la quantité de sel et d’abrasif dépend du temps. Le problème est basé sur l’hypothèse que le modèle de prédiction est capable de fournir la quantité d’épandage pour chaque segment et pour chaque heure avec une bonne précision. Le fait d’avoir cette information pour chaque heure et pour chaque segment de rue, introduit la notion du temps dépendant. Le nouveau problème est modélisé à l’aide d’une formulation mathématique sur le graphe original, ce qui présente un défi de modélisation. En effet, il est difficile d’associer des temps de début et de fin uniques à un arc ou à une arête. Une métaheuristique basée sur la stratégie de destruction et construction a été développée pour résoudre les grandes instances. La métaheuristique est inspirée de SISRs (Slack Induction by String Removals). Elle considère la demande dépendante du temps et la présence des arêtes par la méthode d’évaluation basée sur la programmation dynamique. De nouvelles instances ont été créées à partir des instances des problèmes de tournées de véhicules générales avec contrainte de capacité avec demande fixe. Elles ont été générées à partir de différents types de fonction dont la demande dépend du temps. La troisième contribution propose une nouvelle approche, dans le but de présenter le niveau de priorité des rues (la hiérarchie de service) sous forme d’une fonction linéaire dépendante du temps. Le problème présenté dans cette contribution concerne des tournées de véhicules générales hiérarchiques avec contrainte de capacité sous l’incertitude de la demande. Lorsque les données collectées ne permettent pas de développer un bon modèle de prédiction, la notion de demande dépendante du temps n’est plus valide. L’approche robuste a démontré une grande réussite pour traiter et résoudre les problèmes avec incertitude. Une métaheuristique robuste a été proposée pour résoudre les deux cas réels de Granby et de Saint-Jean-sur-Richelieu. La métaheuristique a été validée par un modèle mathématique sur les petites instances générées à partir des cas réels. La simulation de Monte Carlo a été utilisée pour évaluer les différentes solutions proposées. En outre, elle permet d’offrir aux gestionnaires un outil de décision pour comparer les différentes solutions robustes, et aussi pour comprendre le compromis entre le niveau de robustesse souhaité et d’autres mesures de performances (coût, risque, niveau de service).----------ABSTRACT : This thesis combines two different fields applied to winter road maintenance : operational research and data science. Data science was used to develop a prediction model for the quantity of salt and abrasive with a machine learning methodology, later this model is considered for building vehicles routing. This route planning was developed using operational research which seeks to optimize routes by looking at several constraints and by integrating real data. The thesis which is the fruit of a collaboration with two Canadian cities Granby and Saint-Jean-sur-Richelieu, deals with a real application in winter road maintenance which is the spreading operation. The spreading operation presents an activity necessary for winter road maintenance, in order to ensure better road traffic. However, this road safety comes with a significant economic and environmental cost, which creates a great concern in order to reduce the economic and environmental impact. This thesis contributes significantly in the spreading operations : firstly, predicting the necessary quantity of salt and abrasive to be spread in order to avoid over-spreading, secondly optimizing the spreading operations routes considering quantity variations. The first contribution of this thesis is to develop a prediction model for the quantities of salt and abrasive using machine learning algorithms, for each street segment and for each hour. The importance of this contribution lies in the integration of geomatic data with weather-road data, and also the feature engineering. Several machine learning algorithms were evaluated (Random Forest, Extremely Random Trees, Artificial Neural Networks, Adaboost, Gradient Boosting Machine and XGBoost); ultimately XGBoost performed better. The prediction model not only predicts the amounts of salt and abrasive needed to spread, but also identifies the most important variables in the model. This information presents an interesting decision-making tool for managers. The identification of important variables could improve snow removal operations. According to the results, the human factor (driver) significantly influences the amount of spreading, so controlling this factor can significantly improve the spreading operations.The second contribution introduces a new problem in the literature : the mixed capacitated general routing problem with time-dependent demand; the problem is based on the assumption that the prediction model is able to provide the amount of spreading for each segment and for each hour with good accuracy. Having this information for each hour and for each street segment introduces the concept of time dependency. The new problem was modeled using a mathematical formulation on the original graph, which presents a modeling challenge since it is difficult to associate a unique starting and ending time to an arc or edge. A meta-heuristic based on the destruction and construction strategy has been developed to solve large-scale instances. The meta-heuristic is inspired by SISRs considers time-dependent demand and the presence of edges by an evaluation method based on dynamic programming. New instances were created from the instances of the mixed capacitated general routing problem with fixed demand; the new instances were generated from different types of function where the demand varies with time. The third contribution proposes a new approach to present the service hierarchy or the priority level of streets, as a time-dependent linear function. The problem addressed in this contribution concerns the hierarchical mixed capacitated general routing problems under demand uncertainty. When the collected data does not allow the development of a good prediction model, the concept of time-dependent demand is no longer valid. The robust approach has demonstrated great success in resolving and dealing with problems with uncertainty. A robust meta-heuristic was proposed to solve the two real cases Granby and Saint-Jean-sur-Richelieu, the meta-heuristic was validated by a mathematical model on small instances generated from the real cases. The Monte Carlo simulation was used, on the one hand, to evaluate the different solutions proposed, and, on the other hand, to offer managers a decision tool to compare the different robust solutions and also to understand the trade-off between the desired level of robustness, and other performance measures (cost, risk, level of service)

    GAPS: a hybridised framework applied to vehicle routing problems

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    In this thesis we consider two combinatorial optimisation problems; the Capacitated Vehicle Routing Problem (CVRP) and the Capacitated Arc Routing Problem (CARP). In the CVRP, the objective is to find a set of routes for a homogenous fleet of vehicles, which must service a set of customers from a central depot. In contrast, the CARP requires a set of routes for a fleet of vehicles to service a set of customers at the street level of an intercity network. After a comprehensive discussion of the existing exact and heuristic algorithmic techniques presented in the literature for these problems, computational experiments to provide a benchmark comparison of a subset of algorithmic implementations for these methods are presented for both the CVRP and CARP, run against a series of dataset instances from the literature. All dataset instances are re-catalogued using a standard format to overcome the difficulties of the different naming schemes and duplication of instances that exist between different sources. We then present a framework, which we shall call Genetic Algorithm with Perturbation Scheme (GAPS), to solve a number of combinatorial optimisation problems. The idea is to use a genetic algorithm as a container framework in conjunction with a perturbation or weight coding scheme. These schemes make alterations to the underlying input data within a problem instance, after which the changed data is fed into a standard problem specific heuristic and the solution obtained decoded to give a true solution cost using the original unaltered instance data. We first present GAPS in a generic context, using the Travelling Salesman Problem (TSP) as an example and then provide details of the specific application of GAPS to both the CVRP and CARP. Computational experiments on a large set of problem instances from the literature are presented and comparisons with the results achieved by the current state of the art algorithmic approaches for both problems are given, highlighting the robustness and effectiveness of the GAPS framewor

    GestĂŁo de rotas na recolha de resĂ­duos

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    Tese de mestrado. Mestrado Integrado em Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

    Multiple Drone and Truck Arc Routing Problem

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    In this thesis we introduce the Multiple Drone and Truck Arc Routing Problem (MDTARP) which considers a fleet of drones used in synchronization with a ground vehicle to provide service to edges in a network within a given time-horizon. Drones launch from a ground vehicle to perform services on a set of edges and return to recharge batteries ready for its next trip. We formulate a multi-objective mathematical model that maximizes coverage of edges on a network based on given weights while minimizing unnecessary travel by drones and the ground vehicle. We develop an Iterated Local Search heuristic and a Cluster-based Location Search heuristic and assess their performance on several instances representing different scenarios. We compare results with solutions obtained using a commercial solver to showcase the effectiveness of the heuristics and perform computational experiments to provide recommendations for the users
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